Last updated: 2021-09-14

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Knit directory: soil_alcontar/

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Prepare data

raw_soil <- readxl::read_excel(here::here("data/Resultados_Suelos_2018_2021_v2.xlsx"), 
    sheet = "SEGUIMIENTO_SUELOS_sin_ouliers") %>% janitor::clean_names() %>% mutate(treatment_name = case_when(str_detect(geo_parcela_nombre, 
    "NP_") ~ "Autumn Burning / No Browsing", str_detect(geo_parcela_nombre, "PR_") ~ 
    "Spring Burning / Browsing", str_detect(geo_parcela_nombre, "P_") ~ "Autumn Burning / Browsing"), 
    zona = case_when(str_detect(geo_parcela_nombre, "NP_") ~ "QOt_NP", str_detect(geo_parcela_nombre, 
        "PR_") ~ "QPr_P", str_detect(geo_parcela_nombre, "P_") ~ "QOt_P"), fecha = lubridate::ymd(fecha), 
    pre_post_quema = case_when(pre_post_quema == "Prequema" ~ "0 preQuema", pre_post_quema == 
        "Postquema" ~ "1 postQuema"))
  • Select data pre- and intermediately post-fire (first post-fire sampling: “2018-12-20” and “2019-05-09” for autumn and spring fires respectively)
soil <- raw_soil %>% filter(fecha %in% lubridate::ymd(c("2018-12-11", "2018-12-20", 
    "2019-04-24", "2019-05-09"))) %>% mutate(zona = as.factor(zona), pre_post_quema = as.factor(pre_post_quema))
  • Structure of the data
              zona
pre_post_quema QOt_NP QOt_P QPr_P
   0 preQuema      24    24    24
   1 postQuema     24    24    24

Modelize

  • For each response variable, the approach modelling is

\(Y \sim zona (P|NP|PR) + Fecha(pre|post) + zona \times Fecha\)

  • using the “(1|zona:geo_parcela_nombre)” as nested random effects

  • Then explore error distribution of the variable response and model diagnostics

  • Select the appropiate error distribution and use LMM or GLMM

  • Explore Post-hoc

  • Plot interactions

Humedad

Model

Type III Analysis of Variance Table with Satterthwaite's method
                    Sum Sq Mean Sq NumDF DenDF F value    Pr(>F)    
pre_post_quema      236.42 236.416     1   129 26.1050 1.136e-06 ***
zona                  1.46   0.728     2     9  0.0803    0.9235    
pre_post_quema:zona 462.22 231.109     2   129 25.5190 4.593e-10 ***
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
                        Sum Sq     Mean Sq NumDF DenDF     F value       Pr(>F)
pre_post_quema      236.415514 236.4155136     1   129 26.10495006 1.135929e-06
zona                  1.455293   0.7276463     2     9  0.08034655 9.234509e-01
pre_post_quema:zona 462.217132 231.1085659     2   129 25.51895804 4.592612e-10
                    variable              factor
pre_post_quema       humedad      pre_post_quema
zona                 humedad                zona
pre_post_quema:zona  humedad pre_post_quema:zona

Post-hoc

$`emmeans of pre_post_quema`
 pre_post_quema emmean    SE   df lower.CL upper.CL
 0 preQuema       13.4 0.676 12.1    11.88     14.8
 1 postQuema      10.8 0.676 12.1     9.32     12.3

Results are averaged over the levels of: zona 
Degrees-of-freedom method: kenward-roger 
Confidence level used: 0.95 

$`pairwise differences of pre_post_quema`
 1                        estimate    SE  df t.ratio p.value
 0 preQuema - 1 postQuema     2.56 0.502 129 5.109   <.0001 

Results are averaged over the levels of: zona 
Degrees-of-freedom method: kenward-roger 
$`emmeans of zona`
 zona   emmean   SE df lower.CL upper.CL
 QOt_NP   12.4 1.09  9     9.97     14.9
 QOt_P    11.9 1.09  9     9.46     14.4
 QPr_P    11.9 1.09  9     9.41     14.3

Results are averaged over the levels of: pre_post_quema 
Degrees-of-freedom method: kenward-roger 
Confidence level used: 0.95 

$`pairwise differences of zona`
 1              estimate   SE df t.ratio p.value
 QOt_NP - QOt_P   0.5060 1.54  9 0.329   0.9424 
 QOt_NP - QPr_P   0.5579 1.54  9 0.363   0.9306 
 QOt_P - QPr_P    0.0518 1.54  9 0.034   0.9994 

Results are averaged over the levels of: pre_post_quema 
Degrees-of-freedom method: kenward-roger 
P value adjustment: tukey method for comparing a family of 3 estimates 
$`emmeans of pre_post_quema | zona`
zona = QOt_NP:
 pre_post_quema emmean   SE   df lower.CL upper.CL
 0 preQuema      11.99 1.17 12.1     9.44     14.5
 1 postQuema     12.86 1.17 12.1    10.31     15.4

zona = QOt_P:
 pre_post_quema emmean   SE   df lower.CL upper.CL
 0 preQuema      12.45 1.17 12.1     9.90     15.0
 1 postQuema     11.39 1.17 12.1     8.84     13.9

zona = QPr_P:
 pre_post_quema emmean   SE   df lower.CL upper.CL
 0 preQuema      15.62 1.17 12.1    13.07     18.2
 1 postQuema      8.11 1.17 12.1     5.57     10.7

Degrees-of-freedom method: kenward-roger 
Confidence level used: 0.95 

$`pairwise differences of pre_post_quema | zona`
zona = QOt_NP:
 2                        estimate    SE  df t.ratio p.value
 0 preQuema - 1 postQuema   -0.872 0.869 129 -1.004  0.3171 

zona = QOt_P:
 2                        estimate    SE  df t.ratio p.value
 0 preQuema - 1 postQuema    1.054 0.869 129  1.213  0.2273 

zona = QPr_P:
 2                        estimate    SE  df t.ratio p.value
 0 preQuema - 1 postQuema    7.507 0.869 129  8.641  <.0001 

Degrees-of-freedom method: kenward-roger 

CIC

Model

Type III Analysis of Variance Table with Satterthwaite's method
                    Sum Sq Mean Sq NumDF DenDF F value   Pr(>F)   
pre_post_quema      31.174 31.1736     1   129  8.7568 0.003671 **
zona                40.297 20.1484     2     9  5.6598 0.025614 * 
pre_post_quema:zona 19.681  9.8403     2   129  2.7642 0.066764 . 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
                      Sum Sq   Mean Sq NumDF DenDF  F value      Pr(>F)
pre_post_quema      31.17361 31.173609     1   129 8.756838 0.003670913
zona                40.29686 20.148428     2     9 5.659804 0.025613698
pre_post_quema:zona 19.68056  9.840278     2   129 2.764188 0.066763596
                    variable              factor
pre_post_quema           cic      pre_post_quema
zona                     cic                zona
pre_post_quema:zona      cic pre_post_quema:zona

Post-hoc

$`emmeans of pre_post_quema`
 pre_post_quema emmean    SE   df lower.CL upper.CL
 0 preQuema       15.5 0.462 11.5     14.4     16.5
 1 postQuema      14.5 0.462 11.5     13.5     15.5

Results are averaged over the levels of: zona 
Degrees-of-freedom method: kenward-roger 
Confidence level used: 0.95 

$`pairwise differences of pre_post_quema`
 1                        estimate    SE  df t.ratio p.value
 0 preQuema - 1 postQuema    0.931 0.314 129 2.959   0.0037 

Results are averaged over the levels of: zona 
Degrees-of-freedom method: kenward-roger 
$`emmeans of zona`
 zona   emmean    SE df lower.CL upper.CL
 QOt_NP   15.4 0.753  9     13.7     17.1
 QOt_P    16.6 0.753  9     14.9     18.3
 QPr_P    13.0 0.753  9     11.3     14.7

Results are averaged over the levels of: pre_post_quema 
Degrees-of-freedom method: kenward-roger 
Confidence level used: 0.95 

$`pairwise differences of zona`
 1              estimate   SE df t.ratio p.value
 QOt_NP - QOt_P    -1.19 1.06  9 -1.115  0.5291 
 QOt_NP - QPr_P     2.33 1.06  9  2.191  0.1262 
 QOt_P - QPr_P      3.52 1.06  9  3.307  0.0225 

Results are averaged over the levels of: pre_post_quema 
Degrees-of-freedom method: kenward-roger 
P value adjustment: tukey method for comparing a family of 3 estimates 
$`emmeans of pre_post_quema | zona`
zona = QOt_NP:
 pre_post_quema emmean    SE   df lower.CL upper.CL
 0 preQuema       15.7 0.801 11.5     13.9     17.4
 1 postQuema      15.1 0.801 11.5     13.3     16.8

zona = QOt_P:
 pre_post_quema emmean    SE   df lower.CL upper.CL
 0 preQuema       17.5 0.801 11.5     15.8     19.3
 1 postQuema      15.6 0.801 11.5     13.8     17.3

zona = QPr_P:
 pre_post_quema emmean    SE   df lower.CL upper.CL
 0 preQuema       13.2 0.801 11.5     11.4     14.9
 1 postQuema      12.9 0.801 11.5     11.2     14.7

Degrees-of-freedom method: kenward-roger 
Confidence level used: 0.95 

$`pairwise differences of pre_post_quema | zona`
zona = QOt_NP:
 2                        estimate    SE  df t.ratio p.value
 0 preQuema - 1 postQuema    0.583 0.545 129 1.071   0.2862 

zona = QOt_P:
 2                        estimate    SE  df t.ratio p.value
 0 preQuema - 1 postQuema    1.958 0.545 129 3.595   0.0005 

zona = QPr_P:
 2                        estimate    SE  df t.ratio p.value
 0 preQuema - 1 postQuema    0.250 0.545 129 0.459   0.6470 

Degrees-of-freedom method: kenward-roger 

C

Model

Type III Analysis of Variance Table with Satterthwaite's method
                     Sum Sq Mean Sq NumDF DenDF F value   Pr(>F)   
pre_post_quema      17.1120 17.1120     1   129  7.8770 0.005784 **
zona                 5.3708  2.6854     2     9  1.2361 0.335481   
pre_post_quema:zona  0.5673  0.2837     2   129  0.1306 0.877707   
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
                        Sum Sq   Mean Sq NumDF DenDF   F value     Pr(>F)
pre_post_quema      17.1120115 17.112011     1   129 7.8770265 0.00578378
zona                 5.3707569  2.685378     2     9 1.2361374 0.33548133
pre_post_quema:zona  0.5673181  0.283659     2   129 0.1305743 0.87770709
                     variable              factor
pre_post_quema      c_percent      pre_post_quema
zona                c_percent                zona
pre_post_quema:zona c_percent pre_post_quema:zona

Post-hoc

$`emmeans of pre_post_quema`
 pre_post_quema emmean    SE   df lower.CL upper.CL
 0 preQuema       7.16 0.405 10.9     6.27     8.06
 1 postQuema      7.85 0.405 10.9     6.96     8.75

Results are averaged over the levels of: zona 
Degrees-of-freedom method: kenward-roger 
Confidence level used: 0.95 

$`pairwise differences of pre_post_quema`
 1                        estimate    SE  df t.ratio p.value
 0 preQuema - 1 postQuema   -0.689 0.246 129 -2.807  0.0058 

Results are averaged over the levels of: zona 
Degrees-of-freedom method: kenward-roger 
$`emmeans of zona`
 zona   emmean    SE df lower.CL upper.CL
 QOt_NP   6.89 0.669  9     5.37     8.40
 QOt_P    8.33 0.669  9     6.82     9.84
 QPr_P    7.31 0.669  9     5.80     8.82

Results are averaged over the levels of: pre_post_quema 
Degrees-of-freedom method: kenward-roger 
Confidence level used: 0.95 

$`pairwise differences of zona`
 1              estimate    SE df t.ratio p.value
 QOt_NP - QOt_P   -1.446 0.945  9 -1.529  0.3233 
 QOt_NP - QPr_P   -0.424 0.945  9 -0.448  0.8965 
 QOt_P - QPr_P     1.022 0.945  9  1.081  0.5483 

Results are averaged over the levels of: pre_post_quema 
Degrees-of-freedom method: kenward-roger 
P value adjustment: tukey method for comparing a family of 3 estimates 
$`emmeans of pre_post_quema | zona`
zona = QOt_NP:
 pre_post_quema emmean    SE   df lower.CL upper.CL
 0 preQuema       6.50 0.702 10.9     4.96     8.05
 1 postQuema      7.27 0.702 10.9     5.72     8.81

zona = QOt_P:
 pre_post_quema emmean    SE   df lower.CL upper.CL
 0 preQuema       7.94 0.702 10.9     6.39     9.48
 1 postQuema      8.73 0.702 10.9     7.18    10.27

zona = QPr_P:
 pre_post_quema emmean    SE   df lower.CL upper.CL
 0 preQuema       7.05 0.702 10.9     5.51     8.60
 1 postQuema      7.57 0.702 10.9     6.02     9.11

Degrees-of-freedom method: kenward-roger 
Confidence level used: 0.95 

$`pairwise differences of pre_post_quema | zona`
zona = QOt_NP:
 2                        estimate    SE  df t.ratio p.value
 0 preQuema - 1 postQuema   -0.765 0.425 129 -1.799  0.0744 

zona = QOt_P:
 2                        estimate    SE  df t.ratio p.value
 0 preQuema - 1 postQuema   -0.790 0.425 129 -1.858  0.0655 

zona = QPr_P:
 2                        estimate    SE  df t.ratio p.value
 0 preQuema - 1 postQuema   -0.512 0.425 129 -1.205  0.2306 

Degrees-of-freedom method: kenward-roger 

Fe

Model

Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]
Mixed Model Anova Table (Type 3 tests, KR-method)

Model: fe_percent ~ pre_post_quema * zona + (1 | zona:geo_parcela_nombre)
Data: df_model
               Effect     df    F p.value
1      pre_post_quema 1, 129 0.52    .473
2                zona   2, 9 0.69    .526
3 pre_post_quema:zona 2, 129 0.17    .845
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]
Mixed Model Anova Table (Type 3 tests, KR-method)

Model: fe_percent ~ pre_post_quema * zona + (1 | zona:geo_parcela_nombre)
Data: df_model
                    num Df den Df      F Pr(>F)
pre_post_quema           1    129 0.5186 0.4727
zona                     2      9 0.6903 0.5261
pre_post_quema:zona      2    129 0.1688 0.8449

Post-hoc

$`emmeans of pre_post_quema`
 pre_post_quema emmean      SE  df asymp.LCL asymp.UCL
 0 preQuema      0.549 0.00599 Inf     0.537     0.561
 1 postQuema     0.560 0.00818 Inf     0.544     0.576

Results are averaged over the levels of: zona 
Results are given on the inverse (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of pre_post_quema`
 1                        estimate      SE  df z.ratio p.value
 0 preQuema - 1 postQuema  -0.0112 0.00566 Inf -1.979  0.0478 

Results are averaged over the levels of: zona 
Note: contrasts are still on the inverse scale 
$`emmeans of zona`
 zona   emmean      SE  df asymp.LCL asymp.UCL
 QOt_NP  0.526 0.00599 Inf     0.514     0.538
 QOt_P   0.607 0.00848 Inf     0.590     0.623
 QPr_P   0.531 0.00846 Inf     0.514     0.547

Results are averaged over the levels of: pre_post_quema 
Results are given on the inverse (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of zona`
 1              estimate      SE  df z.ratio p.value
 QOt_NP - QOt_P -0.08067 0.00609 Inf -13.241 <.0001 
 QOt_NP - QPr_P -0.00486 0.00608 Inf  -0.799 0.7036 
 QOt_P - QPr_P   0.07581 0.00860 Inf   8.819 <.0001 

Results are averaged over the levels of: pre_post_quema 
Note: contrasts are still on the inverse scale 
P value adjustment: tukey method for comparing a family of 3 estimates 
$`emmeans of pre_post_quema | zona`
zona = QOt_NP:
 pre_post_quema emmean      SE  df asymp.LCL asymp.UCL
 0 preQuema      0.515 0.00543 Inf     0.504     0.526
 1 postQuema     0.537 0.00747 Inf     0.522     0.552

zona = QOt_P:
 pre_post_quema emmean      SE  df asymp.LCL asymp.UCL
 0 preQuema      0.601 0.00768 Inf     0.586     0.616
 1 postQuema     0.612 0.01058 Inf     0.591     0.633

zona = QPr_P:
 pre_post_quema emmean      SE  df asymp.LCL asymp.UCL
 0 preQuema      0.530 0.00767 Inf     0.515     0.545
 1 postQuema     0.531 0.01054 Inf     0.511     0.552

Results are given on the inverse (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of pre_post_quema | zona`
zona = QOt_NP:
 2                        estimate      SE  df z.ratio p.value
 0 preQuema - 1 postQuema -0.02193 0.00519 Inf -4.225  <.0001 

zona = QOt_P:
 2                        estimate      SE  df z.ratio p.value
 0 preQuema - 1 postQuema -0.01062 0.00737 Inf -1.442  0.1493 

zona = QPr_P:
 2                        estimate      SE  df z.ratio p.value
 0 preQuema - 1 postQuema -0.00106 0.00732 Inf -0.144  0.8853 

Note: contrasts are still on the inverse scale 

MO

Model

Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]
Mixed Model Anova Table (Type 3 tests, KR-method)

Model: mo ~ pre_post_quema * zona + (1 | zona:geo_parcela_nombre)
Data: df_model
               Effect     df         F p.value
1      pre_post_quema 1, 129      1.35    .247
2                zona   2, 9 20.37 ***   <.001
3 pre_post_quema:zona 2, 129      1.04    .357
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]
Mixed Model Anova Table (Type 3 tests, KR-method)

Model: mo ~ pre_post_quema * zona + (1 | zona:geo_parcela_nombre)
Data: df_model
                    num Df den Df       F    Pr(>F)    
pre_post_quema           1    129  1.3496 0.2474987    
zona                     2      9 20.3726 0.0004557 ***
pre_post_quema:zona      2    129  1.0383 0.3569810    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Post-hoc

$`emmeans of pre_post_quema`
 pre_post_quema emmean     SE  df asymp.LCL asymp.UCL
 0 preQuema      0.244 0.0152 Inf     0.215     0.274
 1 postQuema     0.233 0.0149 Inf     0.203     0.262

Results are averaged over the levels of: zona 
Results are given on the inverse (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of pre_post_quema`
 1                        estimate     SE  df z.ratio p.value
 0 preQuema - 1 postQuema   0.0117 0.0177 Inf 0.660   0.5094 

Results are averaged over the levels of: zona 
Note: contrasts are still on the inverse scale 
$`emmeans of zona`
 zona   emmean     SE  df asymp.LCL asymp.UCL
 QOt_NP  0.194 0.0186 Inf     0.157     0.230
 QOt_P   0.168 0.0176 Inf     0.133     0.202
 QPr_P   0.354 0.0254 Inf     0.305     0.404

Results are averaged over the levels of: pre_post_quema 
Results are given on the inverse (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of zona`
 1              estimate     SE  df z.ratio p.value
 QOt_NP - QOt_P   0.0261 0.0253 Inf  1.034  0.5556 
 QOt_NP - QPr_P  -0.1608 0.0312 Inf -5.156  <.0001 
 QOt_P - QPr_P   -0.1870 0.0307 Inf -6.084  <.0001 

Results are averaged over the levels of: pre_post_quema 
Note: contrasts are still on the inverse scale 
P value adjustment: tukey method for comparing a family of 3 estimates 
$`emmeans of pre_post_quema | zona`
zona = QOt_NP:
 pre_post_quema emmean     SE  df asymp.LCL asymp.UCL
 0 preQuema      0.213 0.0231 Inf     0.168     0.258
 1 postQuema     0.174 0.0206 Inf     0.134     0.215

zona = QOt_P:
 pre_post_quema emmean     SE  df asymp.LCL asymp.UCL
 0 preQuema      0.170 0.0204 Inf     0.130     0.210
 1 postQuema     0.165 0.0201 Inf     0.126     0.205

zona = QPr_P:
 pre_post_quema emmean     SE  df asymp.LCL asymp.UCL
 0 preQuema      0.350 0.0330 Inf     0.286     0.415
 1 postQuema     0.359 0.0337 Inf     0.293     0.425

Results are given on the inverse (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of pre_post_quema | zona`
zona = QOt_NP:
 2                        estimate     SE  df z.ratio p.value
 0 preQuema - 1 postQuema  0.03837 0.0233 Inf  1.648  0.0993 

zona = QOt_P:
 2                        estimate     SE  df z.ratio p.value
 0 preQuema - 1 postQuema  0.00480 0.0201 Inf  0.239  0.8111 

zona = QPr_P:
 2                        estimate     SE  df z.ratio p.value
 0 preQuema - 1 postQuema -0.00816 0.0432 Inf -0.189  0.8502 

Note: contrasts are still on the inverse scale 

K

Model

Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]
Mixed Model Anova Table (Type 3 tests, KR-method)

Model: k_percent ~ pre_post_quema * zona + (1 | zona:geo_parcela_nombre)
Data: df_model
               Effect     df      F p.value
1      pre_post_quema 1, 129   2.19    .141
2                zona   2, 9 6.73 *    .016
3 pre_post_quema:zona 2, 129   0.43    .651
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]
Mixed Model Anova Table (Type 3 tests, KR-method)

Model: k_percent ~ pre_post_quema * zona + (1 | zona:geo_parcela_nombre)
Data: df_model
                    num Df den Df      F Pr(>F)  
pre_post_quema           1    129 2.1884 0.1415  
zona                     2      9 6.7329 0.0163 *
pre_post_quema:zona      2    129 0.4302 0.6513  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Post-hoc

$`emmeans of pre_post_quema`
 pre_post_quema emmean    SE  df asymp.LCL asymp.UCL
 0 preQuema       2.13 0.168 Inf      1.80      2.46
 1 postQuema      2.37 0.173 Inf      2.03      2.71

Results are averaged over the levels of: zona 
Results are given on the inverse (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of pre_post_quema`
 1                        estimate    SE  df z.ratio p.value
 0 preQuema - 1 postQuema   -0.243 0.107 Inf -2.275  0.0229 

Results are averaged over the levels of: zona 
Note: contrasts are still on the inverse scale 
$`emmeans of zona`
 zona   emmean    SE  df asymp.LCL asymp.UCL
 QOt_NP   2.01 0.273 Inf     1.472      2.54
 QOt_P    3.25 0.257 Inf     2.746      3.75
 QPr_P    1.49 0.303 Inf     0.896      2.08

Results are averaged over the levels of: pre_post_quema 
Results are given on the inverse (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of zona`
 1              estimate    SE  df z.ratio p.value
 QOt_NP - QOt_P   -1.242 0.372 Inf -3.339  0.0024 
 QOt_NP - QPr_P    0.517 0.406 Inf  1.273  0.4106 
 QOt_P - QPr_P     1.759 0.396 Inf  4.445  <.0001 

Results are averaged over the levels of: pre_post_quema 
Note: contrasts are still on the inverse scale 
P value adjustment: tukey method for comparing a family of 3 estimates 
$`emmeans of pre_post_quema | zona`
zona = QOt_NP:
 pre_post_quema emmean    SE  df asymp.LCL asymp.UCL
 0 preQuema       1.99 0.283 Inf     1.438      2.55
 1 postQuema      2.02 0.283 Inf     1.466      2.58

zona = QOt_P:
 pre_post_quema emmean    SE  df asymp.LCL asymp.UCL
 0 preQuema       2.93 0.276 Inf     2.386      3.47
 1 postQuema      3.57 0.301 Inf     2.982      4.16

zona = QPr_P:
 pre_post_quema emmean    SE  df asymp.LCL asymp.UCL
 0 preQuema       1.46 0.307 Inf     0.860      2.06
 1 postQuema      1.52 0.308 Inf     0.914      2.12

Results are given on the inverse (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of pre_post_quema | zona`
zona = QOt_NP:
 2                        estimate    SE  df z.ratio p.value
 0 preQuema - 1 postQuema  -0.0297 0.150 Inf -0.198  0.8433 

zona = QOt_P:
 2                        estimate    SE  df z.ratio p.value
 0 preQuema - 1 postQuema  -0.6437 0.263 Inf -2.444  0.0145 

zona = QPr_P:
 2                        estimate    SE  df z.ratio p.value
 0 preQuema - 1 postQuema  -0.0556 0.104 Inf -0.536  0.5921 

Note: contrasts are still on the inverse scale 

Mg

Model

Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]
Mixed Model Anova Table (Type 3 tests, KR-method)

Model: mg_percent ~ pre_post_quema * zona + (1 | zona:geo_parcela_nombre)
Data: df_model
               Effect     df      F p.value
1      pre_post_quema 1, 129   1.37    .245
2                zona   2, 9 3.05 +    .097
3 pre_post_quema:zona 2, 129   0.84    .434
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]
Mixed Model Anova Table (Type 3 tests, KR-method)

Model: mg_percent ~ pre_post_quema * zona + (1 | zona:geo_parcela_nombre)
Data: df_model
                    num Df den Df      F  Pr(>F)  
pre_post_quema           1    129 1.3653 0.24477  
zona                     2      9 3.0516 0.09734 .
pre_post_quema:zona      2    129 0.8402 0.43395  
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Post-hoc

$`emmeans of pre_post_quema`
 pre_post_quema emmean     SE  df asymp.LCL asymp.UCL
 0 preQuema      0.784 0.0807 Inf     0.625     0.942
 1 postQuema     0.749 0.0803 Inf     0.592     0.907

Results are averaged over the levels of: zona 
Results are given on the inverse (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of pre_post_quema`
 1                        estimate     SE  df z.ratio p.value
 0 preQuema - 1 postQuema   0.0345 0.0371 Inf 0.929   0.3528 

Results are averaged over the levels of: zona 
Note: contrasts are still on the inverse scale 
$`emmeans of zona`
 zona   emmean    SE  df asymp.LCL asymp.UCL
 QOt_NP  0.759 0.133 Inf     0.498      1.02
 QOt_P   0.992 0.125 Inf     0.747      1.24
 QPr_P   0.548 0.144 Inf     0.265      0.83

Results are averaged over the levels of: pre_post_quema 
Results are given on the inverse (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of zona`
 1              estimate    SE  df z.ratio p.value
 QOt_NP - QOt_P   -0.233 0.181 Inf -1.286  0.4032 
 QOt_NP - QPr_P    0.212 0.196 Inf  1.080  0.5264 
 QOt_P - QPr_P     0.445 0.190 Inf  2.341  0.0504 

Results are averaged over the levels of: pre_post_quema 
Note: contrasts are still on the inverse scale 
P value adjustment: tukey method for comparing a family of 3 estimates 
$`emmeans of pre_post_quema | zona`
zona = QOt_NP:
 pre_post_quema emmean    SE  df asymp.LCL asymp.UCL
 0 preQuema      0.807 0.137 Inf     0.538     1.076
 1 postQuema     0.711 0.135 Inf     0.446     0.977

zona = QOt_P:
 pre_post_quema emmean    SE  df asymp.LCL asymp.UCL
 0 preQuema      0.988 0.132 Inf     0.730     1.247
 1 postQuema     0.996 0.132 Inf     0.737     1.255

zona = QPr_P:
 pre_post_quema emmean    SE  df asymp.LCL asymp.UCL
 0 preQuema      0.555 0.146 Inf     0.269     0.841
 1 postQuema     0.540 0.146 Inf     0.255     0.826

Results are given on the inverse (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of pre_post_quema | zona`
zona = QOt_NP:
 2                        estimate     SE  df z.ratio p.value
 0 preQuema - 1 postQuema  0.09632 0.0566 Inf  1.702  0.0887 

zona = QOt_P:
 2                        estimate     SE  df z.ratio p.value
 0 preQuema - 1 postQuema -0.00743 0.0841 Inf -0.088  0.9296 

zona = QPr_P:
 2                        estimate     SE  df z.ratio p.value
 0 preQuema - 1 postQuema  0.01456 0.0461 Inf  0.316  0.7523 

Note: contrasts are still on the inverse scale 

C/N

Model

Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]
Mixed Model Anova Table (Type 3 tests, KR-method)

Model: c_n ~ pre_post_quema * zona + (1 | zona:geo_parcela_nombre)
Data: df_model
               Effect     df    F p.value
1      pre_post_quema 1, 129 0.32    .574
2                zona   2, 9 0.43    .663
3 pre_post_quema:zona 2, 129 0.42    .659
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]
Mixed Model Anova Table (Type 3 tests, KR-method)

Model: c_n ~ pre_post_quema * zona + (1 | zona:geo_parcela_nombre)
Data: df_model
                    num Df den Df      F Pr(>F)
pre_post_quema           1    129 0.3175 0.5741
zona                     2      9 0.4310 0.6626
pre_post_quema:zona      2    129 0.4183 0.6591

Post-hoc

$`emmeans of pre_post_quema`
 pre_post_quema emmean      SE  df asymp.LCL asymp.UCL
 0 preQuema     0.0269 0.00248 Inf    0.0221    0.0318
 1 postQuema    0.0281 0.00250 Inf    0.0232    0.0330

Results are averaged over the levels of: zona 
Results are given on the inverse (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of pre_post_quema`
 1                        estimate     SE  df z.ratio p.value
 0 preQuema - 1 postQuema -0.00119 0.0016 Inf -0.743  0.4574 

Results are averaged over the levels of: zona 
Note: contrasts are still on the inverse scale 
$`emmeans of zona`
 zona   emmean      SE  df asymp.LCL asymp.UCL
 QOt_NP 0.0301 0.00398 Inf    0.0223    0.0379
 QOt_P  0.0275 0.00403 Inf    0.0196    0.0354
 QPr_P  0.0249 0.00402 Inf    0.0171    0.0328

Results are averaged over the levels of: pre_post_quema 
Results are given on the inverse (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of zona`
 1              estimate      SE  df z.ratio p.value
 QOt_NP - QOt_P  0.00257 0.00559 Inf 0.459   0.8904 
 QOt_NP - QPr_P  0.00514 0.00562 Inf 0.915   0.6311 
 QOt_P - QPr_P   0.00257 0.00566 Inf 0.455   0.8923 

Results are averaged over the levels of: pre_post_quema 
Note: contrasts are still on the inverse scale 
P value adjustment: tukey method for comparing a family of 3 estimates 
$`emmeans of pre_post_quema | zona`
zona = QOt_NP:
 pre_post_quema emmean      SE  df asymp.LCL asymp.UCL
 0 preQuema     0.0290 0.00422 Inf    0.0207    0.0373
 1 postQuema    0.0312 0.00431 Inf    0.0227    0.0396

zona = QOt_P:
 pre_post_quema emmean      SE  df asymp.LCL asymp.UCL
 0 preQuema     0.0263 0.00421 Inf    0.0180    0.0345
 1 postQuema    0.0288 0.00429 Inf    0.0204    0.0372

zona = QPr_P:
 pre_post_quema emmean      SE  df asymp.LCL asymp.UCL
 0 preQuema     0.0255 0.00424 Inf    0.0172    0.0338
 1 postQuema    0.0244 0.00420 Inf    0.0162    0.0326

Results are given on the inverse (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of pre_post_quema | zona`
zona = QOt_NP:
 2                        estimate      SE  df z.ratio p.value
 0 preQuema - 1 postQuema -0.00215 0.00305 Inf -0.706  0.4803 

zona = QOt_P:
 2                        estimate      SE  df z.ratio p.value
 0 preQuema - 1 postQuema -0.00252 0.00269 Inf -0.939  0.3479 

zona = QPr_P:
 2                        estimate      SE  df z.ratio p.value
 0 preQuema - 1 postQuema  0.00111 0.00255 Inf  0.435  0.6635 

Note: contrasts are still on the inverse scale 

P

Model

Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]
Mixed Model Anova Table (Type 3 tests, KR-method)

Model: p ~ pre_post_quema * zona + (1 | zona:geo_parcela_nombre)
Data: df_model
               Effect     df       F p.value
1      pre_post_quema 1, 129  3.20 +    .076
2                zona   2, 9  3.81 +    .063
3 pre_post_quema:zona 2, 129 4.88 **    .009
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]
Mixed Model Anova Table (Type 3 tests, KR-method)

Model: p ~ pre_post_quema * zona + (1 | zona:geo_parcela_nombre)
Data: df_model
                    num Df den Df      F   Pr(>F)   
pre_post_quema           1    129 3.2017 0.075909 . 
zona                     2      9 3.8067 0.063389 . 
pre_post_quema:zona      2    129 4.8757 0.009093 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Post-hoc

$`emmeans of pre_post_quema`
 pre_post_quema emmean     SE  df asymp.LCL asymp.UCL
 0 preQuema       1.53 0.0628 Inf      1.40      1.65
 1 postQuema      1.68 0.0588 Inf      1.57      1.80

Results are averaged over the levels of: zona 
Results are given on the log (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of pre_post_quema`
 1                        estimate     SE  df z.ratio p.value
 0 preQuema - 1 postQuema   -0.156 0.0744 Inf -2.103  0.0355 

Results are averaged over the levels of: zona 
Results are given on the log (not the response) scale. 
$`emmeans of zona`
 zona   emmean     SE  df asymp.LCL asymp.UCL
 QOt_NP   1.52 0.0844 Inf      1.35      1.69
 QOt_P    1.46 0.0870 Inf      1.29      1.64
 QPr_P    1.83 0.0776 Inf      1.68      1.98

Results are averaged over the levels of: pre_post_quema 
Results are given on the log (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of zona`
 1              estimate    SE  df z.ratio p.value
 QOt_NP - QOt_P   0.0548 0.121 Inf  0.451  0.8938 
 QOt_NP - QPr_P  -0.3116 0.114 Inf -2.734  0.0172 
 QOt_P - QPr_P   -0.3664 0.116 Inf -3.147  0.0047 

Results are averaged over the levels of: pre_post_quema 
Results are given on the log (not the response) scale. 
P value adjustment: tukey method for comparing a family of 3 estimates 
$`emmeans of pre_post_quema | zona`
zona = QOt_NP:
 pre_post_quema emmean     SE  df asymp.LCL asymp.UCL
 0 preQuema       1.34 0.1122 Inf      1.12      1.56
 1 postQuema      1.70 0.0998 Inf      1.50      1.90

zona = QOt_P:
 pre_post_quema emmean     SE  df asymp.LCL asymp.UCL
 0 preQuema       1.34 0.1165 Inf      1.11      1.57
 1 postQuema      1.59 0.1053 Inf      1.38      1.79

zona = QPr_P:
 pre_post_quema emmean     SE  df asymp.LCL asymp.UCL
 0 preQuema       1.90 0.0936 Inf      1.72      2.08
 1 postQuema      1.76 0.0989 Inf      1.57      1.96

Results are given on the log (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of pre_post_quema | zona`
zona = QOt_NP:
 2                        estimate    SE  df z.ratio p.value
 0 preQuema - 1 postQuema   -0.361 0.129 Inf -2.808  0.0050 

zona = QOt_P:
 2                        estimate    SE  df z.ratio p.value
 0 preQuema - 1 postQuema   -0.247 0.138 Inf -1.790  0.0734 

zona = QPr_P:
 2                        estimate    SE  df z.ratio p.value
 0 preQuema - 1 postQuema    0.139 0.114 Inf  1.217  0.2235 

Results are given on the log (not the response) scale. 

N

Model

Post-hoc

$`emmeans of pre_post_quema`
 pre_post_quema emmean     SE  df lower.CL upper.CL
 0 preQuema      -1.31 0.0710 136    -1.45   -1.171
 1 postQuema     -1.10 0.0678 136    -1.24   -0.968

Results are averaged over the levels of: zona 
Results are given on the logit (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of pre_post_quema`
 1                        estimate     SE  df t.ratio p.value
 0 preQuema - 1 postQuema    -0.21 0.0968 136 -2.170  0.0318 

Results are averaged over the levels of: zona 
Results are given on the log odds ratio (not the response) scale. 
$`emmeans of zona`
 zona   emmean     SE  df lower.CL upper.CL
 QOt_NP  -1.11 0.0827 136    -1.27   -0.947
 QOt_P   -1.12 0.0828 136    -1.28   -0.953
 QPr_P   -1.39 0.0882 136    -1.57   -1.219

Results are averaged over the levels of: pre_post_quema 
Results are given on the logit (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of zona`
 1              estimate    SE  df t.ratio p.value
 QOt_NP - QOt_P  0.00552 0.116 136 0.048   0.9988 
 QOt_NP - QPr_P  0.28266 0.120 136 2.362   0.0510 
 QOt_P - QPr_P   0.27714 0.120 136 2.315   0.0571 

Results are averaged over the levels of: pre_post_quema 
Results are given on the log odds ratio (not the response) scale. 
P value adjustment: tukey method for comparing a family of 3 estimates 
$`emmeans of pre_post_quema | zona`
zona = QOt_NP:
 pre_post_quema emmean    SE  df lower.CL upper.CL
 0 preQuema     -1.282 0.121 136    -1.52   -1.043
 1 postQuema    -0.940 0.112 136    -1.16   -0.718

zona = QOt_P:
 pre_post_quema emmean    SE  df lower.CL upper.CL
 0 preQuema     -1.269 0.120 136    -1.51   -1.031
 1 postQuema    -0.964 0.113 136    -1.19   -0.741

zona = QPr_P:
 pre_post_quema emmean    SE  df lower.CL upper.CL
 0 preQuema     -1.384 0.124 136    -1.63   -1.140
 1 postQuema    -1.403 0.124 136    -1.65   -1.157

Results are given on the logit (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of pre_post_quema | zona`
zona = QOt_NP:
 2                        estimate    SE  df t.ratio p.value
 0 preQuema - 1 postQuema  -0.3425 0.164 136 -2.086  0.0388 

zona = QOt_P:
 2                        estimate    SE  df t.ratio p.value
 0 preQuema - 1 postQuema  -0.3055 0.164 136 -1.860  0.0650 

zona = QPr_P:
 2                        estimate    SE  df t.ratio p.value
 0 preQuema - 1 postQuema   0.0182 0.174 136  0.104  0.9171 

Results are given on the log odds ratio (not the response) scale. 

Na

Model

Post-hoc

$`emmeans of pre_post_quema`
 pre_post_quema emmean     SE  df lower.CL upper.CL
 0 preQuema      -3.00 0.0916 136    -3.18    -2.82
 1 postQuema     -3.08 0.0934 136    -3.26    -2.90

Results are averaged over the levels of: zona 
Results are given on the logit (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of pre_post_quema`
 1                        estimate     SE  df t.ratio p.value
 0 preQuema - 1 postQuema    0.082 0.0891 136 0.920   0.3593 

Results are averaged over the levels of: zona 
Results are given on the log odds ratio (not the response) scale. 
$`emmeans of zona`
 zona   emmean    SE  df lower.CL upper.CL
 QOt_NP  -2.88 0.134 136    -3.15    -2.61
 QOt_P   -3.43 0.146 136    -3.72    -3.15
 QPr_P   -2.80 0.133 136    -3.07    -2.54

Results are averaged over the levels of: pre_post_quema 
Results are given on the logit (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of zona`
 1              estimate    SE  df t.ratio p.value
 QOt_NP - QOt_P   0.5523 0.196 136  2.816  0.0153 
 QOt_NP - QPr_P  -0.0772 0.188 136 -0.411  0.9113 
 QOt_P - QPr_P   -0.6295 0.195 136 -3.224  0.0045 

Results are averaged over the levels of: pre_post_quema 
Results are given on the log odds ratio (not the response) scale. 
P value adjustment: tukey method for comparing a family of 3 estimates 
$`emmeans of pre_post_quema | zona`
zona = QOt_NP:
 pre_post_quema emmean    SE  df lower.CL upper.CL
 0 preQuema      -2.86 0.152 136    -3.16    -2.56
 1 postQuema     -2.91 0.154 136    -3.21    -2.60

zona = QOt_P:
 pre_post_quema emmean    SE  df lower.CL upper.CL
 0 preQuema      -3.36 0.168 136    -3.69    -3.03
 1 postQuema     -3.51 0.173 136    -3.85    -3.17

zona = QPr_P:
 pre_post_quema emmean    SE  df lower.CL upper.CL
 0 preQuema      -2.78 0.150 136    -3.08    -2.48
 1 postQuema     -2.83 0.151 136    -3.13    -2.53

Results are given on the logit (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of pre_post_quema | zona`
zona = QOt_NP:
 2                        estimate    SE  df t.ratio p.value
 0 preQuema - 1 postQuema   0.0499 0.144 136 0.345   0.7303 

zona = QOt_P:
 2                        estimate    SE  df t.ratio p.value
 0 preQuema - 1 postQuema   0.1486 0.177 136 0.837   0.4039 

zona = QPr_P:
 2                        estimate    SE  df t.ratio p.value
 0 preQuema - 1 postQuema   0.0475 0.139 136 0.343   0.7321 

Results are given on the log odds ratio (not the response) scale. 

pH agua

Model

Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]
Mixed Model Anova Table (Type 3 tests, KR-method)

Model: p_h_agua_eez ~ pre_post_quema * zona + (1 | zona:geo_parcela_nombre)
Data: df_model
               Effect     df       F p.value
1      pre_post_quema 1, 129    0.01    .928
2                zona   2, 9  4.64 *    .041
3 pre_post_quema:zona 2, 129 5.20 **    .007
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]
Mixed Model Anova Table (Type 3 tests, KR-method)

Model: p_h_agua_eez ~ pre_post_quema * zona + (1 | zona:geo_parcela_nombre)
Data: df_model
                    num Df den Df      F   Pr(>F)   
pre_post_quema           1    129 0.0083 0.927741   
zona                     2      9 4.6444 0.041140 * 
pre_post_quema:zona      2    129 5.2024 0.006716 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Post-hoc

$`emmeans of pre_post_quema`
 pre_post_quema emmean        SE  df asymp.LCL asymp.UCL
 0 preQuema     0.1255 0.0002922 Inf    0.1249    0.1261
 1 postQuema    0.1255 0.0002926 Inf    0.1250    0.1261

Results are averaged over the levels of: zona 
Results are given on the inverse (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of pre_post_quema`
 1                         estimate       SE  df z.ratio p.value
 0 preQuema - 1 postQuema -4.66e-05 0.000375 Inf -0.124  0.9012 

Results are averaged over the levels of: zona 
Note: contrasts are still on the inverse scale 
$`emmeans of zona`
 zona   emmean       SE  df asymp.LCL asymp.UCL
 QOt_NP  0.126 0.000389 Inf     0.125     0.127
 QOt_P   0.126 0.000389 Inf     0.125     0.127
 QPr_P   0.125 0.000387 Inf     0.124     0.125

Results are averaged over the levels of: pre_post_quema 
Results are given on the inverse (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of zona`
 1              estimate       SE  df z.ratio p.value
 QOt_NP - QOt_P 0.000164 0.000550 Inf 0.299   0.9519 
 QOt_NP - QPr_P 0.001311 0.000548 Inf 2.393   0.0441 
 QOt_P - QPr_P  0.001147 0.000549 Inf 2.090   0.0920 

Results are averaged over the levels of: pre_post_quema 
Note: contrasts are still on the inverse scale 
P value adjustment: tukey method for comparing a family of 3 estimates 
$`emmeans of pre_post_quema | zona`
zona = QOt_NP:
 pre_post_quema emmean       SE  df asymp.LCL asymp.UCL
 0 preQuema      0.126 0.000503 Inf     0.125     0.127
 1 postQuema     0.126 0.000509 Inf     0.125     0.127

zona = QOt_P:
 pre_post_quema emmean       SE  df asymp.LCL asymp.UCL
 0 preQuema      0.125 0.000506 Inf     0.124     0.126
 1 postQuema     0.126 0.000509 Inf     0.125     0.127

zona = QPr_P:
 pre_post_quema emmean       SE  df asymp.LCL asymp.UCL
 0 preQuema      0.126 0.000507 Inf     0.125     0.127
 1 postQuema     0.124 0.000501 Inf     0.123     0.125

Results are given on the inverse (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of pre_post_quema | zona`
zona = QOt_NP:
 2                         estimate       SE  df z.ratio p.value
 0 preQuema - 1 postQuema -0.000834 0.000649 Inf -1.285  0.1989 

zona = QOt_P:
 2                         estimate       SE  df z.ratio p.value
 0 preQuema - 1 postQuema -0.001003 0.000652 Inf -1.539  0.1238 

zona = QPr_P:
 2                         estimate       SE  df z.ratio p.value
 0 preQuema - 1 postQuema  0.001697 0.000646 Inf  2.628  0.0086 

Note: contrasts are still on the inverse scale 

pH KCl

Model

Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]
Mixed Model Anova Table (Type 3 tests, KR-method)

Model: p_h_k_cl ~ pre_post_quema * zona + (1 | zona:geo_parcela_nombre)
Data: df_model
               Effect     df         F p.value
1      pre_post_quema 1, 129 37.25 ***   <.001
2                zona   2, 9      2.57    .131
3 pre_post_quema:zona 2, 129   6.18 **    .003
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]
Mixed Model Anova Table (Type 3 tests, KR-method)

Model: p_h_k_cl ~ pre_post_quema * zona + (1 | zona:geo_parcela_nombre)
Data: df_model
                    num Df den Df      F    Pr(>F)    
pre_post_quema           1    129 37.246 1.133e-08 ***
zona                     2      9  2.573  0.130687    
pre_post_quema:zona      2    129  6.183  0.002728 ** 
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Post-hoc

$`emmeans of pre_post_quema`
 pre_post_quema emmean       SE  df asymp.LCL asymp.UCL
 0 preQuema      0.134 0.000592 Inf     0.133     0.135
 1 postQuema     0.131 0.000591 Inf     0.130     0.133

Results are averaged over the levels of: zona 
Results are given on the inverse (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of pre_post_quema`
 1                        estimate       SE  df z.ratio p.value
 0 preQuema - 1 postQuema  0.00224 0.000359 Inf 6.253   <.0001 

Results are averaged over the levels of: zona 
Note: contrasts are still on the inverse scale 
$`emmeans of zona`
 zona   emmean       SE  df asymp.LCL asymp.UCL
 QOt_NP  0.133 0.000973 Inf     0.131     0.135
 QOt_P   0.133 0.000974 Inf     0.131     0.135
 QPr_P   0.131 0.000980 Inf     0.129     0.133

Results are averaged over the levels of: pre_post_quema 
Results are given on the inverse (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of zona`
 1              estimate      SE  df z.ratio p.value
 QOt_NP - QOt_P 9.55e-05 0.00138 Inf 0.069   0.9973 
 QOt_NP - QPr_P 2.21e-03 0.00138 Inf 1.603   0.2444 
 QOt_P - QPr_P  2.12e-03 0.00138 Inf 1.533   0.2755 

Results are averaged over the levels of: pre_post_quema 
Note: contrasts are still on the inverse scale 
P value adjustment: tukey method for comparing a family of 3 estimates 
$`emmeans of pre_post_quema | zona`
zona = QOt_NP:
 pre_post_quema emmean      SE  df asymp.LCL asymp.UCL
 0 preQuema      0.134 0.00102 Inf     0.132     0.136
 1 postQuema     0.133 0.00102 Inf     0.131     0.135

zona = QOt_P:
 pre_post_quema emmean      SE  df asymp.LCL asymp.UCL
 0 preQuema      0.134 0.00102 Inf     0.132     0.136
 1 postQuema     0.132 0.00102 Inf     0.130     0.134

zona = QPr_P:
 pre_post_quema emmean      SE  df asymp.LCL asymp.UCL
 0 preQuema      0.133 0.00103 Inf     0.131     0.135
 1 postQuema     0.129 0.00102 Inf     0.127     0.131

Results are given on the inverse (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of pre_post_quema | zona`
zona = QOt_NP:
 2                        estimate       SE  df z.ratio p.value
 0 preQuema - 1 postQuema 0.000697 0.000624 Inf 1.116   0.2644 

zona = QOt_P:
 2                        estimate       SE  df z.ratio p.value
 0 preQuema - 1 postQuema 0.002241 0.000624 Inf 3.588   0.0003 

zona = QPr_P:
 2                        estimate       SE  df z.ratio p.value
 0 preQuema - 1 postQuema 0.003792 0.000614 Inf 6.173   <.0001 

Note: contrasts are still on the inverse scale 

NH4

  • prepara datos
              zona
pre_post_quema QOt_NP QOt_P
   0 preQuema      24    24
   1 postQuema     21    22

Model

Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]
Mixed Model Anova Table (Type 3 tests, KR-method)

Model: n_nh4 ~ pre_post_quema * zona + (1 | zona:geo_parcela_nombre)
Data: df_model
               Effect       df         F p.value
1      pre_post_quema 1, 81.31 42.84 ***   <.001
2                zona  1, 6.01      2.19    .189
3 pre_post_quema:zona 1, 81.31      2.53    .115
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]
Mixed Model Anova Table (Type 3 tests, KR-method)

Model: n_nh4 ~ pre_post_quema * zona + (1 | zona:geo_parcela_nombre)
Data: df_model
                    num Df  den Df       F    Pr(>F)    
pre_post_quema           1 81.3060 42.8372 4.956e-09 ***
zona                     1  6.0102  2.1923    0.1891    
pre_post_quema:zona      1 81.3060  2.5343    0.1153    
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Post-hoc

$`emmeans of pre_post_quema`
 pre_post_quema emmean     SE  df asymp.LCL asymp.UCL
 0 preQuema      1.636 0.1547 Inf     1.332     1.939
 1 postQuema     0.383 0.0587 Inf     0.268     0.498

Results are averaged over the levels of: zona 
Results are given on the inverse (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of pre_post_quema`
 1                        estimate    SE  df z.ratio p.value
 0 preQuema - 1 postQuema     1.25 0.153 Inf 8.158   <.0001 

Results are averaged over the levels of: zona 
Note: contrasts are still on the inverse scale 
$`emmeans of zona`
 zona   emmean    SE  df asymp.LCL asymp.UCL
 QOt_NP  0.996 0.125 Inf     0.752      1.24
 QOt_P   1.023 0.123 Inf     0.783      1.26

Results are averaged over the levels of: pre_post_quema 
Results are given on the inverse (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of zona`
 1              estimate    SE  df z.ratio p.value
 QOt_NP - QOt_P  -0.0273 0.173 Inf -0.158  0.8748 

Results are averaged over the levels of: pre_post_quema 
Note: contrasts are still on the inverse scale 
$`emmeans of pre_post_quema | zona`
zona = QOt_NP:
 pre_post_quema emmean     SE  df asymp.LCL asymp.UCL
 0 preQuema      1.682 0.2245 Inf     1.242     2.122
 1 postQuema     0.310 0.0708 Inf     0.171     0.449

zona = QOt_P:
 pre_post_quema emmean     SE  df asymp.LCL asymp.UCL
 0 preQuema      1.589 0.2121 Inf     1.174     2.005
 1 postQuema     0.457 0.0878 Inf     0.285     0.629

Results are given on the inverse (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of pre_post_quema | zona`
zona = QOt_NP:
 2                        estimate    SE  df z.ratio p.value
 0 preQuema - 1 postQuema     1.37 0.221 Inf 6.214   <.0001 

zona = QOt_P:
 2                        estimate    SE  df z.ratio p.value
 0 preQuema - 1 postQuema     1.13 0.213 Inf 5.324   <.0001 

Note: contrasts are still on the inverse scale 

NO3

Model

Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]
Mixed Model Anova Table (Type 3 tests, KR-method)

Model: n_no3 ~ pre_post_quema * zona + (1 | zona:geo_parcela_nombre)
Data: df_model
               Effect       df    F p.value
1      pre_post_quema 1, 81.11 0.93    .339
2                zona  1, 6.01 0.22    .658
3 pre_post_quema:zona 1, 81.11 0.25    .619
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1
Fitting one lmer() model. [DONE]
Calculating p-values. [DONE]
Mixed Model Anova Table (Type 3 tests, KR-method)

Model: n_no3 ~ pre_post_quema * zona + (1 | zona:geo_parcela_nombre)
Data: df_model
                    num Df  den Df      F Pr(>F)
pre_post_quema           1 81.1146 0.9263 0.3387
zona                     1  6.0094 0.2164 0.6581
pre_post_quema:zona      1 81.1146 0.2492 0.6190

Post-hoc

$`emmeans of pre_post_quema`
 pre_post_quema emmean    SE  df asymp.LCL asymp.UCL
 0 preQuema       1.17 0.137 Inf     0.899      1.44
 1 postQuema      1.28 0.144 Inf     1.001      1.57

Results are averaged over the levels of: zona 
Results are given on the inverse (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of pre_post_quema`
 1                        estimate    SE  df z.ratio p.value
 0 preQuema - 1 postQuema   -0.116 0.116 Inf -1.004  0.3155 

Results are averaged over the levels of: zona 
Note: contrasts are still on the inverse scale 
$`emmeans of zona`
 zona   emmean    SE  df asymp.LCL asymp.UCL
 QOt_NP   1.29 0.181 Inf     0.934      1.64
 QOt_P    1.16 0.175 Inf     0.819      1.50

Results are averaged over the levels of: pre_post_quema 
Results are given on the inverse (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of zona`
 1              estimate    SE  df z.ratio p.value
 QOt_NP - QOt_P    0.127 0.247 Inf 0.513   0.6078 

Results are averaged over the levels of: pre_post_quema 
Note: contrasts are still on the inverse scale 
$`emmeans of pre_post_quema | zona`
zona = QOt_NP:
 pre_post_quema emmean    SE  df asymp.LCL asymp.UCL
 0 preQuema       1.26 0.196 Inf     0.874      1.64
 1 postQuema      1.32 0.204 Inf     0.920      1.72

zona = QOt_P:
 pre_post_quema emmean    SE  df asymp.LCL asymp.UCL
 0 preQuema       1.08 0.185 Inf     0.713      1.44
 1 postQuema      1.25 0.198 Inf     0.859      1.64

Results are given on the inverse (not the response) scale. 
Confidence level used: 0.95 

$`pairwise differences of pre_post_quema | zona`
zona = QOt_NP:
 2                        estimate    SE  df z.ratio p.value
 0 preQuema - 1 postQuema  -0.0616 0.170 Inf -0.362  0.7170 

zona = QOt_P:
 2                        estimate    SE  df z.ratio p.value
 0 preQuema - 1 postQuema  -0.1710 0.157 Inf -1.086  0.2775 

Note: contrasts are still on the inverse scale 

General Overview

Mean + SE table

Characteristic 0 preQuema 1 postQuema
QOt_NP, N = 241 QOt_P, N = 241 QPr_P, N = 241 QOt_NP, N = 241 QOt_P, N = 241 QPr_P, N = 241
humedad 11.99 (0.76) 12.45 (0.66) 15.62 (0.63) 12.86 (0.86) 11.39 (0.79) 8.11 (0.53)
n_nh4 0.60 (0.05) 0.63 (0.05) NA (NA) 3.42 (0.57) 2.44 (0.45) NA (NA)
n_no3 0.85 (0.11) 0.98 (0.08) NA (NA) 0.81 (0.12) 0.82 (0.08) NA (NA)
fe_percent 1.97 (0.08) 1.76 (0.12) 1.95 (0.08) 1.89 (0.04) 1.72 (0.07) 1.95 (0.09)
k_percent 0.55 (0.04) 0.35 (0.03) 0.79 (0.06) 0.54 (0.03) 0.29 (0.02) 0.75 (0.06)
mg_percent 1.46 (0.15) 1.11 (0.09) 2.00 (0.14) 1.74 (0.21) 1.10 (0.07) 2.06 (0.18)
na_percent 0.05 (0.01) 0.03 (0.00) 0.06 (0.01) 0.05 (0.00) 0.03 (0.00) 0.06 (0.01)
n_percent 0.22 (0.02) 0.21 (0.02) 0.19 (0.02) 0.30 (0.04) 0.28 (0.03) 0.18 (0.01)
c_percent 6.50 (0.33) 7.94 (0.46) 7.05 (0.31) 7.27 (0.42) 8.73 (0.35) 7.57 (0.38)
c_n 37.15 (4.17) 42.29 (3.66) 41.62 (3.62) 34.30 (5.37) 37.98 (4.00) 43.72 (3.06)
cic 15.67 (0.38) 17.54 (0.49) 13.17 (0.49) 15.08 (0.36) 15.58 (0.47) 12.92 (0.55)
p 3.83 (0.23) 3.84 (0.24) 6.75 (0.92) 5.50 (0.33) 4.91 (0.35) 5.88 (0.25)
mo 4.77 (0.39) 5.97 (0.44) 2.87 (0.32) 5.86 (0.65) 6.15 (0.46) 2.80 (0.16)
p_h_k_cl 7.48 (0.02) 7.44 (0.03) 7.52 (0.04) 7.52 (0.03) 7.57 (0.03) 7.74 (0.03)
p_h_agua_eez 7.96 (0.03) 7.98 (0.03) 7.97 (0.04) 7.91 (0.02) 7.92 (0.03) 8.07 (0.02)

1 Mean (std.error)

Figures

Version Author Date
71c36dd ajpelu 2021-09-10
46f8fd8 ajpelu 2021-09-07

Version Author Date
71c36dd ajpelu 2021-09-10

Version Author Date
71c36dd ajpelu 2021-09-10

Anovas table

zona
fecha
zona X fecha
Variables F p F p F p
c_n 0.431 0.663 0.318 0.574 0.418 0.659
cic 5.660 0.026 8.757 0.004 2.764 0.067
k_percent 6.733 0.016 2.188 0.141 0.430 0.651
mg_percent 3.052 0.097 1.365 0.245 0.840 0.434
mo 20.373 0.000 1.350 0.247 1.038 0.357
n_nh4 2.192 0.189 42.837 0.000 2.534 0.115
n_no3 0.216 0.658 0.926 0.339 0.249 0.619
p 3.807 0.063 3.202 0.076 4.876 0.009
p_h_agua_eez 4.644 0.041 0.008 0.928 5.202 0.007
p_h_k_cl 2.573 0.131 37.246 0.000 6.183 0.003
humedad 0.080 0.923 26.105 0.000 25.519 0.000
n_percent 7.301 0.026 5.122 0.024 2.697 0.260
c_percent 1.236 0.335 7.877 0.006 0.131 0.878
na_percent 11.955 0.003 0.697 0.404 0.241 0.887

R version 4.0.2 (2020-06-22)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.3

Matrix products: default
BLAS:   /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] kableExtra_1.3.1   gtsummary_1.4.2    plotrix_3.8-1      glmmTMB_1.0.2.1   
 [5] afex_0.28-1        performance_0.7.2  multcomp_1.4-16    TH.data_1.0-10    
 [9] mvtnorm_1.1-1      emmeans_1.5.4      lmerTest_3.1-3     lme4_1.1-27.1     
[13] Matrix_1.3-2       fitdistrplus_1.1-3 survival_3.2-7     MASS_7.3-53       
[17] ggpubr_0.4.0       janitor_2.1.0      here_1.0.1         forcats_0.5.1     
[21] stringr_1.4.0      dplyr_1.0.6        purrr_0.3.4        readr_1.4.0       
[25] tidyr_1.1.3        tibble_3.1.2       ggplot2_3.3.5      tidyverse_1.3.1   
[29] rmdformats_1.0.1   knitr_1.31         workflowr_1.6.2   

loaded via a namespace (and not attached):
  [1] minqa_1.2.4         colorspace_2.0-0    ggsignif_0.6.0     
  [4] ellipsis_0.3.2      rio_0.5.16          rprojroot_2.0.2    
  [7] estimability_1.3    snakecase_0.11.0    fs_1.5.0           
 [10] rstudioapi_0.13     farver_2.0.3        fansi_0.4.2        
 [13] lubridate_1.7.10    xml2_1.3.2          codetools_0.2-18   
 [16] splines_4.0.2       jsonlite_1.7.2      nloptr_1.2.2.2     
 [19] gt_0.3.0            pbkrtest_0.5-0.1    broom_0.7.9        
 [22] dbplyr_2.1.1        compiler_4.0.2      httr_1.4.2         
 [25] backports_1.2.1     assertthat_0.2.1    fastmap_1.1.0      
 [28] cli_2.5.0           formatR_1.8         later_1.1.0.1      
 [31] htmltools_0.5.2     tools_4.0.2         coda_0.19-4        
 [34] gtable_0.3.0        glue_1.4.2          reshape2_1.4.4     
 [37] Rcpp_1.0.7          carData_3.0-4       cellranger_1.1.0   
 [40] jquerylib_0.1.3     vctrs_0.3.8         nlme_3.1-152       
 [43] broom.helpers_1.3.0 insight_0.14.4      xfun_0.23          
 [46] openxlsx_4.2.3      rvest_1.0.0         lifecycle_1.0.0    
 [49] rstatix_0.6.0       zoo_1.8-8           scales_1.1.1       
 [52] hms_1.0.0           promises_1.2.0.1    parallel_4.0.2     
 [55] sandwich_3.0-0      TMB_1.7.19          yaml_2.2.1         
 [58] curl_4.3            sass_0.3.1          stringi_1.7.4      
 [61] highr_0.8           checkmate_2.0.0     boot_1.3-26        
 [64] zip_2.1.1           commonmark_1.7      rlang_0.4.10       
 [67] pkgconfig_2.0.3     evaluate_0.14       lattice_0.20-41    
 [70] labeling_0.4.2      tidyselect_1.1.1    plyr_1.8.6         
 [73] magrittr_2.0.1      bookdown_0.21.6     R6_2.5.0           
 [76] generics_0.1.0      DBI_1.1.1           pillar_1.6.1       
 [79] haven_2.3.1         whisker_0.4         foreign_0.8-81     
 [82] withr_2.4.1         abind_1.4-5         modelr_0.1.8       
 [85] crayon_1.4.1        car_3.0-10          utf8_1.1.4         
 [88] rmarkdown_2.8       grid_4.0.2          readxl_1.3.1       
 [91] data.table_1.14.0   git2r_0.28.0        webshot_0.5.2      
 [94] reprex_2.0.0        digest_0.6.27       xtable_1.8-4       
 [97] httpuv_1.5.5        numDeriv_2016.8-1.1 munsell_0.5.0      
[100] viridisLite_0.3.0   bslib_0.2.4